ORBSLAM输出的轨迹与数据集真实轨迹进行空间轨迹对齐并输出txt文件C++代码

以下是一个简单的C++代码示例，用于将ORB-SLAM输出的轨迹与数据集真实轨迹进行空间轨迹对齐并输出txt文件：

#include <iostream>
#include <fstream>
#include <string>
#include <vector>

#include <Eigen/Core>
#include <Eigen/Dense>

using namespace std;
using namespace Eigen;

// 假设ORB-SLAM输出的轨迹存储在文件"orb_slam_trajectory.txt"中，每一行格式为
// timestamp tx ty tz qx qy qz qw
// 其中timestamp为时间戳，tx、ty、tz为相机在世界坐标系下的平移向量，qx、qy、qz、qw为相机在世界坐标系下的四元数表示的旋转
// 假设数据集真实轨迹存储在文件"ground_truth_trajectory.txt"中，每一行格式为
// timestamp tx ty tz qx qy qz qw
// 其中timestamp为时间戳，tx、ty、tz为相机在世界坐标系下的平移向量，qx、qy、qz、qw为相机在世界坐标系下的四元数表示的旋转

int main(int argc, char **argv) {
    // 读取ORB-SLAM输出的轨迹
    vector<double> orb_slam_timestamps, orb_slam_tx, orb_slam_ty, orb_slam_tz, orb_slam_qx, orb_slam_qy, orb_slam_qz, orb_slam_qw;
    ifstream orb_slam_file("orb_slam_trajectory.txt");
    while (orb_slam_file.good()) {
        double timestamp, tx, ty, tz, qx, qy, qz, qw;
        orb_slam_file >> timestamp >> tx >> ty >> tz >> qx >> qy >> qz >> qw;
        if (orb_slam_file.good()) {
            orb_slam_timestamps.push_back(timestamp);
            orb_slam_tx.push_back(tx);
            orb_slam_ty.push_back(ty);
            orb_slam_tz.push_back(tz);
            orb_slam_qx.push_back(qx);
            orb_slam_qy.push_back(qy);
            orb_slam_qz.push_back(qz);
            orb_slam_qw.push_back(qw);
        }
    }
    orb_slam_file.close();

    // 读取真实轨迹
    vector<double> gt_timestamps, gt_tx, gt_ty, gt_tz, gt_qx, gt_qy, gt_qz, gt_qw;
    ifstream gt_file("ground_truth_trajectory.txt");
    while (gt_file.good()) {
        double timestamp, tx, ty, tz, qx, qy, qz, qw;
        gt_file >> timestamp >> tx >> ty >> tz >> qx >> qy >> qz >> qw;
        if (gt_file.good()) {
            gt_timestamps.push_back(timestamp);
            gt_tx.push_back(tx);
            gt_ty.push_back(ty);
            gt_tz.push_back(tz);
            gt_qx.push_back(qx);
            gt_qy.push_back(qy);
            gt_qz.push_back(qz);
            gt_qw.push_back(qw);
        }
    }
    gt_file.close();

    // 空间轨迹对齐
    // 假设ORB-SLAM第一个位姿与真实轨迹第一个位姿的时间戳相同
    double offset_timestamp = orb_slam_timestamps[0];
    int orb_slam_start_index = 0, gt_start_index = 0;
    while (orb_slam_timestamps[orb_slam_start_index] < offset_timestamp) orb_slam_start_index++;
    while (gt_timestamps[gt_start_index] < offset_timestamp) gt_start_index++;
    int num_poses = min(orb_slam_timestamps.size() - orb_slam_start_index, gt_timestamps.size() - gt_start_index);
    MatrixXd orb_slam_poses(4, num_poses), gt_poses(4, num_poses);
    for (int i = 0; i < num_poses; i++) {
        double timestamp = orb_slam_timestamps[orb_slam_start_index + i];
        int gt_index = gt_start_index;
        while (gt_index < gt_timestamps.size() - 1 && gt_timestamps[gt_index + 1] < timestamp) gt_index++;
        double ratio = (timestamp - gt_timestamps[gt_index]) / (gt_timestamps[gt_index + 1] - gt_timestamps[gt_index]);
        Vector3d gt_translation(gt_tx[gt_index] * (1 - ratio) + gt_tx[gt_index + 1] * ratio,
                                gt_ty[gt_index] * (1 - ratio) + gt_ty[gt_index + 1] * ratio,
                                gt_tz[gt_index] * (1 - ratio) + gt_tz[gt_index + 1] * ratio);
        Quaterniond gt_rotation(gt_qw[gt_index] * (1 - ratio) + gt_qw[gt_index + 1] * ratio,
                                gt_qx[gt_index] * (1 - ratio) + gt_qx[gt_index + 1] * ratio,
                                gt_qy[gt_index] * (1 - ratio) + gt_qy[gt_index + 1] * ratio,
                                gt_qz[gt_index] * (1 - ratio) + gt_qz[gt_index + 1] * ratio);
        Vector3d orb_slam_translation(orb_slam_tx[orb_slam_start_index + i], orb_slam_ty[orb_slam_start_index + i], orb_slam_tz[orb_slam_start_index + i]);
        Quaterniond orb_slam_rotation(orb_slam_qw[orb_slam_start_index + i], orb_slam_qx[orb_slam_start_index + i], orb_slam_qy[orb_slam_start_index + i], orb_slam_qz[orb_slam_start_index + i]);
        Vector3d transformed_translation = gt_rotation.inverse() * (orb_slam_rotation * orb_slam_translation);
        Quaterniond transformed_rotation = gt_rotation.inverse() * orb_slam_rotation;
        orb_slam_poses.col(i) << transformed_translation, 1;
        gt_poses.col(i) << gt_translation, 1;
    }
    MatrixXd transformation = orb_slam_poses * gt_poses.transpose() * (gt_poses * gt_poses.transpose()).inverse();

    // 输出对齐后的ORB-SLAM轨迹
    ofstream aligned_file("aligned_trajectory.txt");
    for (int i = 0; i < num_poses; i++) {
        Vector4d aligned_pose = transformation * gt_poses.col(i);
        aligned_file << orb_slam_timestamps[orb_slam_start_index + i] << " " << aligned_pose(0) << " " << aligned_pose(1) << " " << aligned_pose(2) << " " << orb_slam_qx[orb_slam_start_index + i] << " " << orb_slam_qy[orb_slam_start_index + i] << " " << orb_slam_qz[orb_slam_start_index + i] << " " << orb_slam_qw[orb_slam_start_index + i] << endl;
    }
    aligned_file.close();

    return 0;
}

注意：该代码示例假设ORB-SLAM第一个位姿与真实轨迹第一个位姿的时间戳相同。如果不是，请自行修改代码。此外，该代码示例中的空间轨迹对齐方法是使用最小二乘法计算仿射变换矩阵，有时可能会出现较大的误差。如果需要更高精度的对齐结果，请使用更复杂的算法，如ICP等。